Learn about Automation 101 with Epson on December 6 in this free webcast! Register today.

Content from our Sponsor

Robotics Evolves Into the Distribution Center

Data science is behind improvements in machine vision, gripping, and integration, which in turn allow robots to be smarter helpers in distribution centers.

Presented by:

November 07, 2018
Matt Wicks

Today’s logistics robotics market is evolving quickly, with more advanced technologies driving more capable solutions. While we don’t quite have Westworld Hosts or robot maids from The Jetsons, the confluence of advanced technology such as data science and application expertise is providing smart new robotic solutions to longstanding challenges.

Altogether, the full spectrum of robotics technology offers tremendous potential for the distribution center (DC). Breaking through to automate previously manual tasks comes not from a single innovation, but by advances in vision, onboard intelligence, gripping and allied components all coming together in a tightly integrated package.

Get smart

Before looking at the gripping technology, pay attention to the intelligence and perception responsible for putting such tools to use. Simply put, think about the robot’s “brains” – the combination of sensors, data science, and machine learning that enables it to execute tasks.

To understand the importance of the robot’s intelligence, think about a common, rigid gripping tool – a pair of chopsticks. Humans can use these instruments to pick up and move food without much trouble. But this is where robots struggle. Why is that?

Humans have the most advanced perception, cognition, and motion-planning systems in the world. This makes tasks like washing dishes, eating and brushing our teeth seem so effortless.

Robots, on the other hand, lack the necessary perception, cognition, and motion-planning abilities. Vision systems can be designed to find specific items, and we can even design motion-planning algorithms to pick them up.

However, change the item from a hat to a glove, and the vision system fails. Likewise, change the gripping mechanism and the motion planners will struggle.

Teaching robots to think with data science

Data science and machine learning are pushing the capability of robotics systems forward. This addresses how the robot should use grippers for the broad lineup of SKUs and packaging found in the DC.

Properly perceiving a dynamic, unstructured environment like the DC requires processing a lot of data. Take the hat example. Computer vision algorithms can be designed to find the characteristics of a baseball cap, but if we introduce a beanie, our algorithm fails. Vision engineers can update the algorithm to identify the beanie … but adding a cowboy hat requires another development cycle.

On the other hand, a data-driven, machine learning approach uses a massive pool of data from a wide variety of hats to train the system to handle new styles – even if the robot has not seen it before.

The right tools make the job easier

The need to choose the right tool for the job holds true for even the smartest robots, as gripping mechanisms are a key enabler of increased capability. In fact, advances in tooling can make the perception and cognition job much simpler.

Take soft robotics grippers. They use highly compliant materials like those found in living organisms to make it easier to grasp certain objects like tomatoes or eggs. Using this more forgiving gripping technology can make vision and sensing requirements less strict, as soft robotics do not require the same level of precision as rigid grippers to grasp items.

Putting it all together: integration and data

Everyone from massive conglomerates to the hottest new startups can bring a robot into a facility to perform a specific task. But finding a solutions provider that pulls together robotics, data science, and logistics expertise in a complete solution set is rare.

As DCs become increasingly complex, they must consider robotics from a holistic view that accounts for existing systems and overall processes. Thinking of integration as simply implementing a single robot risks losing site-wide benefit.

As robots sets sights on new tasks to automate, the importance of another critical development comes into focus – connectivity. Tracking the progress of logistics tasks requires leveraging the power of connected devices and software to provide real-time data and manage resources and processes for overall output.

Using data to fuel machine learning also plays a critical role in solutions development and performance optimization. A central, cloud-based resource connecting data from myriad systems enables machine learning on a very large scale – across different sites and functions.